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The module is designed to introduce a range of Management Science techniques, it is the level 1 module in the prescriptive analytics stream for the Business Analytics programmes.
This module will describe many of the classical MS problems and solution techniques and illustrate their use and effectiveness. You will have the opportunity to explore the process of understanding, formulating, solving and analysing a number of practical problems using the tools and techniques introduced in the module.

MANG2089 introduces simulation. As an experimental technique, simulation is one the most widely used modelling techniques. This is because, unlike optimising techniques such as queuing theory, it requires few assumptions. As a result, analysts use it to solve a wide variety of complex real-life problems. It is very effective. For example, a quick look at the clients of the Simul8 corporation (http://www.simul8.com/), one of the main simulation software vendors, reveals a long and impressive list of organisations who apply simulation. Students who successfully complete MANG2089 acquire the practical skills needed to conduct a successful simulation project from scratch, and have a theoretical understanding that is essential for the effective use of this powerful decision-aiding tool. Specifically, students will acquire theoretical understanding of and develop practical modelling skills in using three types of simulation:

(i)Monte Carlo simulation to model complex but static problems for which changes over time are not important such as inventory control, forecasting and decision analysis;
(ii)Discrete Event Simulationto model the operational behaviour of systems with complex queues such as hospitals, airports and supermarkets; and
(iii)System dynamics to model long-term, strategic problems such as the long term effects of government policy decisions on the health care system.

MANG2089 introduces simulation. As an experimental technique, simulation is one the most widely used modelling techniques. This is because, unlike optimising techniques such as queuing theory, it requires few assumptions. As a result, analysts use it to solve a wide variety of complex real-life problems. It is very effective. For example, a quick look at the clients of the Simul8 corporation (http://www.simul8.com/), one of the main simulation software vendors, reveals a long and impressive list of organisations who apply simulation. Students who successfully complete MANG2089 acquire the practical skills needed to conduct a successful simulation project from scratch, and have a theoretical understanding that is essential for the effective use of this powerful decision-aiding tool. Specifically, students will acquire theoretical understanding of and develop practical modelling skills in using three types of simulation:

(i)Monte Carlo simulation to model complex but static problems for which changes over time are not important such as inventory control, forecasting and decision analysis;
(ii)Discrete Event Simulationto model the operational behaviour of systems with complex queues such as hospitals, airports and supermarkets; and
(iii)System dynamics to model long-term, strategic problems such as the long term effects of government policy decisions on the health care system.

Organisations are typically faced with many decision problems in the running of their operations and they strive to make better decisions by finding good, or ideally the best (optimal), solutions to such problems. This module is concerned with how decision problems can be formulated mathematically and solved optimally to support the decision making process in organisations. The module will introduce several optimisation techniques and illustrate the application of these techniques on problems from different types of industries.

The techniques introduced in this module have a wide range of applicability on decision problems arising in, among others, resource and workforce planning, business investment, machine scheduling, logistics, and supply chain management. The underlying methods of optimisation studied in this module, however, are generally applicable and not restricted to prescriptive analytics.

Organisations are typically faced with many decision problems in the running of their operations and they strive to make better decisions by finding good, or ideally the best (optimal), solutions to such problems. This module is concerned with how decision problems can be formulated mathematically and solved optimally to support the decision making process in organisations. The module will introduce several optimisation techniques and illustrate the application of these techniques on problems from different types of industries.

The techniques introduced in this module have a wide range of applicability on decision problems arising in, among others, resource and workforce planning, business investment, machine scheduling, logistics, and supply chain management. The underlying methods of optimisation studied in this module, however, are generally applicable and not restricted to prescriptive analytics.

Organisations are typically faced with many decision problems in the running of their operations and they strive to make better decisions by finding good, or ideally the best (optimal), solutions to such problems. This module is concerned with how decision problems can be formulated mathematically and solved optimally to support the decision making process in organisations. The module will introduce several optimisation techniques and illustrate the application of these techniques on problems from different types of industries.

The techniques introduced in this module have a wide range of applicability on decision problems arising in, among others, resource and workforce planning, business investment, machine scheduling, logistics, and supply chain management. The underlying methods of optimisation studied in this module, however, are generally applicable and not restricted to prescriptive analytics.

This is a self-study module that aims to develop the skills required for researching, writing and presenting a report on some aspect of Operational Research/Data and Decision Analytics.

This is a self-study module that aims to develop the skills required for researching, writing and presenting a report on some aspect of Operational Research/Data and Decision Analytics.

In this module we will examine how knowledge about the past is presented in museum exhibition and display. We will look at current practices in exhibition design and discuss the contemporary literature on communicating heritage to a range of audiences. You will then work in groups and present an oral presentation prior to completion of an exhibition proposal on a subject or theme relating to archaeology and heritage.

In this module we will examine how knowledge about the past is presented in museum exhibition and display. We will look at current practices in exhibition design and discuss the contemporary literature on communicating heritage to a range of audiences. You will then work in groups and present an oral presentation prior to completion of an exhibition proposal on a subject or theme relating to archaeology and heritage.